Annual emissions broken down by sector#

Here we can present the visualisations and discuss them.

This page can be easily printed to a pdf using the buttons on the top right hand side of this web page.

This page is just a markdown file that has been slightly adapted to look nicer in Jupyter Books - e.g. removing the cell inputs/ outputs to only show the interactive graph below.

alpha = 0.6
color_ch4 = ["rgba(86, 119, 194, 1)", f"rgba(86, 119, 194, {alpha})"]
color_n2o = ["rgba(99, 182, 137, 1)", f"rgba(99, 182, 137, {alpha})"]
color_fgas = ["rgba(187, 103, 93, 1)", f"rgba(187, 103, 93, {alpha})"]

fig = go.Figure()

mo_logo = base64.b64encode(open("metoffice_logo.png", 'rb').read())
uob_logo = base64.b64encode(open("uob_logo.png", 'rb').read())

def errorbars(df, var, color, dash, name, showlegend=True):

    error_minus=df[var] - df[f"{var}_std"]
    error_plus=df[var] + df[f"{var}_std"]

    fig.add_trace(go.Scatter(
        x=df.index,
        y=error_minus.values,
        fill=None,
        mode='lines',
        line=dict(color=color[1], width=0.1),
        showlegend=False,
        hoverinfo='skip'
        ))

    fig.add_trace(go.Scatter(
        x=df.index,
        y=error_plus.values,
        fill="tonexty",
        mode='lines',
        line=dict(color=color[1], width=0.1),
        showlegend=False,
        hoverinfo='skip'
        ))
    
    fig.add_trace(go.Scatter(
        x=df.index,
        y=df[var],
        mode="lines",
        line=dict(color=color[0], dash=dash),
        showlegend=showlegend,
        name=name,
        hovertemplate = 'InTEM %{x|%Y}: %{y:.0f} Gt yr⁻¹<extra></extra>',
        ))


# errorbars(df_ch4.loc[:"2011-01-01"], "InTEM", color_ch4, None, "CH<sub>4</sub>")
# errorbars(df_ch4.loc["2012-01-01":], "InTEM", color_ch4, None, "CH<sub>4</sub>", showlegend=False)
errorbars(df_ch4, "InTEM", color_ch4, None, "Methane")
fig.add_trace(go.Scatter(
    x=df_ch4.index,
    y=df_ch4["Invent"],
    line=dict(color=color_ch4[0], dash="dot", width=5),
    showlegend=False,
    hovertemplate = 'Inventory %{x|%Y}: %{y:.0f} Gt yr⁻¹<extra></extra>',
    ))

errorbars(df_n2o, "InTEM", color_n2o, None, "Nitrous oxide")
# errorbars(df_n2o[:"2011-01-01"], "InTEM", color_n2o, None, "N<sub>2</sub>O")
# errorbars(df_n2o["2012-01-01":], "InTEM", color_n2o, None, "N<sub>2</sub>O", showlegend=False)
fig.add_trace(go.Scatter(
    x=df_n2o.index,
    y=df_n2o["Invent"],
    line=dict(color=color_n2o[0], dash="dot", width=4),
    showlegend=False,
    hovertemplate = 'Inventory %{x|%Y}: %{y:.0f} Gt yr⁻¹<extra></extra>',
    #name="N<sub>2</sub>O"
    ))

errorbars(df_fgas["2012-01-01":], "InTEM", color_fgas, None, "F-gases")
fig.add_trace(go.Scatter(
    x=df_fgas.index,
    y=df_fgas["Invent"],
    line=dict(color=color_fgas[0], dash="dot", width=4),
    showlegend=False,
    hovertemplate = 'Inventory %{x|%Y}: %{y:.0f} Gt yr⁻¹<extra></extra>',
    #name="F-gases"
    ))

# Add fake line for InTEM
fig.add_trace(go.Scatter(
    x=[pd.Timestamp("1900-01-01"), pd.Timestamp("1900-01-02")],
    y=[0., 0.],
    line=dict(color="black", dash=None),
    marker=dict(opacity=0., size=0),
    showlegend=True,
    name="Atmospheric data"
    ))

# Add fake line for inventory legend
fig.add_trace(go.Scatter(
    x=[pd.Timestamp("1900-01-01"), pd.Timestamp("1900-01-02")],
    y=[0., 0.],
    line=dict(color="black", dash="dot", width=4),
    marker=dict(opacity=0., size=0),
    showlegend=True,
    name="Inventory (2021)"
    ))

fig.update_layout(
    # title={"text": "UK non-CO<sub>2</sub> reenhouse gas emissions 2010 - 2020",
    #         "xanchor": "left",
    #         "x": 0.01},
    yaxis_title="CO<sub>2</sub>-equivalent emissions (Gt yr⁻¹)",
    template="none",
    autosize=False,
    width=1000,
    height=600,
    legend=dict(
        yanchor="top",
        y=0.77,
        xanchor="right",
        x=0.99,
        traceorder="normal"),
    margin=dict(l=100, r=50, t=50, b=50),
    # paper_bgcolor='rgba(0,0,0,0)',
    # plot_bgcolor='rgba(0,0,0,0)'
)

fig.layout.font.size=20
fig.layout.font.family="Arial"

fig.update_xaxes(range=[pd.Timestamp("1990-01-01"),
                        pd.Timestamp("2020-01-01")])

fig.update_yaxes(range=[0,
                        153])

# Add UKMO logo
fig.add_layout_image(
    dict(
        source='data:image/png;base64,{}'.format(mo_logo.decode()),
        xref="x domain",
        yref="y domain",
        x=1.015, y=0.9,
        sizex=0.25,
        sizey=0.3,
        xanchor="right", yanchor="top"
    )
)

# Add UoB logo
fig.add_layout_image(
    dict(
        source='data:image/png;base64,{}'.format(uob_logo.decode()),
        xref="x domain",
        yref="y domain",
        x=0.99, y=0.99,
        sizex=0.2,
        sizey=0.3,
        xanchor="right", yanchor="top"
    )
)

# Add shading to 2-site period
fig.add_vrect(x0="1990-01-01", x1="2011-06-01", 
              #annotation_text=None, annotation_position="left",
              fillcolor="grey", opacity=0.1, line_width=0)

# Text annotation denoting period
fig.add_annotation(
    xref="x",
    yref="y",
    x="2014-01-01",
    y=45,
    align="left",
    text="DECC network",
    showarrow=False,
    font=dict(
        family="Arial",
        size=15,
        color="Grey",
        )
)
fig.add_annotation(
    xref="x",
    yref="y",
    x="2011-06-01",
    arrowwidth=2,
    arrowcolor="grey",
    align="right",
    y=40,
    ax=150,
    ay=0,
    arrowside="start",
    text=None
)

# Text annotation denoting 2-site period
fig.add_annotation(
    xref="x",
    yref="y",
    x="2008-09-01",
    y=40,
    align="right",
    text="Mace Head, Cabauw",
    showarrow=False,
    font=dict(
        family="Arial",
        size=15,
        color="Grey",
        )
)
fig.add_annotation(
    xref="x",
    yref="y",
    x="2011-06-01",
    arrowwidth=2,
    arrowcolor="grey",
    align="right",
    y=35,
    ax=-150,
    ay=0,
    arrowside="start",
    text=None
)


# # Text annotation for CH4
# fig.add_annotation(
#     xref="paper",
#     yref="y",
#     x=1.01,
#     y=df_ch4.loc["2020-01-01", "InTEM"],
#     xanchor="left",
#     align="left",
# #    text="<b>CH<sub>4</sub></b>",
#     text="<b>Methane</b>",
#     showarrow=False,
#     font=dict(
#         family="Arial",
#         size=20,
#         color=color_ch4[0]
#         )
# )

# # Text annotation for N2O
# fig.add_annotation(
#     xref="paper",
#     yref="y",
#     x=1.01,
#     y=df_n2o.loc["2020-01-01", "InTEM"],
#     xanchor="left",
#     align="left",
# #    text="<b>CH<sub>4</sub></b>",
#     text="<b>Nitrous<br>oxide</b>",
#     showarrow=False,
#     font=dict(
#         family="Arial",
#         size=20,
#         color=color_n2o[0]
#         )
# )

# # Text annotation for F-gases
# fig.add_annotation(
#     xref="paper",
#     yref="y",
#     x=1.01,
#     y=df_fgas.loc["2020-01-01", "InTEM"],
#     xanchor="left",
#     align="left",
# #    text="<b>CH<sub>4</sub></b>",
#     text="<b>F-gases</b>",
#     showarrow=False,
#     font=dict(
#         family="Arial",
#         size=20,
#         color=color_fgas[0]
#         )
# )

fig.write_image("InTEM_CO2e_2021.pdf")
fig.write_image("InTEM_CO2e_2021.png")
fig.write_image("InTEM_CO2e_2021.svg")
fig.write_html("InTEM_CO2e_2021.html")

fig.show()

def intem_by_year(year):
        
    Intem_new_year = weighted_temporal_mean(ds_flux, "flux_prior")
    
    intem = Intem_new_year[dict(time=year)] * area
    

    return intem

new_agri2012 = read_invent_ch4("2012", "agric")
new_total2012 = read_invent_ch4("2012", "total")

new_agri_flux2012 = 100*(new_agri2012.flux / new_total2012.flux)

new_agri_flux2012_clip = np.clip(new_agri_flux2012, 0, 100)

new_agri_flux2012_clip.plot()

<matplotlib.collections.QuadMesh at 0x7fd20b0eecd0>
_images/Main_Page_17_1.png 
years = ["2012", "2013", "2014", "2015"]
species = ["1. Energy"]

dataframe = pd.DataFrame(index = years, columns = species)

for name in years:
    
    a = read_invent_ch4(name, "domcom")
    b = read_invent_ch4(name, "energyprod")
    c = read_invent_ch4(name, "offshore")
    d = read_invent_ch4(name, "othertrans")
    e = read_invent_ch4(name, "roadtrans")
    f = read_invent_ch4(name, "total")
    
    fract = a.flux + b.flux + c.flux + d.flux + e.flux / f.flux
    test = fract.reindex_like(Intem2012, method='nearest', tolerance=0.01)
    
    test1 = test.sum()
    test2 = test1.to_pandas()
    
    #dataframe = pd.concat((test2, dataframe))
    
    
    number = 0
    #y = 0
    while number < 4:
        te = test * intem_by_year(number)
        te = te.sum()
        
        te = te * 16 * 31536000 * 28 / 10000000000000
        te = te.to_pandas()
        
        dataframe.loc[years[number]] = te
        
        #pd.DataFrame(te)
        
        #pd.concat([e, te])
        
        number += 1
        #y += 1
dataframe    
#te = xr.concat((te12, te13, te14, te15), dim="time")
#te = te * 16 * 31536000 * 28 / 10000000000000
#te = te.to_pandas()
1. Energy
2012 21660.406571
2013 25066.920315
2014 25012.136709
2015 26557.993559 
def energy_intem(year, time):
    
    a = read_invent_ch4(year, "domcom")
    b = read_invent_ch4(year, "energyprod")
    c = read_invent_ch4(year, "offshore")
    d = read_invent_ch4(year, "othertrans")
    e = read_invent_ch4(year, "roadtrans")
    f = read_invent_ch4(year, "total")
    
    fract = a.flux + b.flux + c.flux + d.flux + e.flux / f.flux
    test = fract.reindex_like(Intem2012, method='nearest', tolerance=0.01)
    
    tenerg = test * intem_by_year(time)
    te = tenerg.sum()
    
    te = te * 16 * 31536000 * 28 / 10000000000000
    te = te.to_pandas()
    
    return te

twaste

time
2012-01-01    27.069449
2013-01-01    24.914120
2014-01-01    23.662646
2015-01-01    23.043143
2016-01-01    23.358710
2017-01-01    23.216320
2018-01-01    23.190191
2019-01-01    23.189085
2020-01-01    23.473584
dtype: float64

ch4_gt_all
1. Energy 1. Energy_std 2. Industrial processes 2. Industrial processes_std 3. Agriculture 3. Agriculture_std 4. Land use, land-use change and forestry 4. Land use, land-use change and forestry_std 5. Waste 5. Waste_std
Year
1990-01-01 17.278187 2.879698 0.134860 0.022477 13.534169 2.255695 2.191062 0.365177 29.021722 4.836954
1991-01-01 17.340445 2.746224 0.129882 0.020569 13.226288 2.094661 2.167182 0.343219 29.016205 4.595327
1992-01-01 15.512368 2.559541 0.123061 0.020305 12.047561 1.987848 1.958975 0.323231 26.358035 4.349076
1993-01-01 15.246390 2.454297 0.121500 0.019559 12.454668 2.004898 2.028496 0.326538 27.548946 4.434708
1994-01-01 14.158017 1.916587 0.147697 0.019994 14.732562 1.994364 2.385312 0.322903 32.696412 4.426152
1995-01-01 13.336143 1.761377 0.111747 0.014759 13.367396 1.765505 2.184826 0.288562 30.359888 4.009797
1996-01-01 15.447600 1.796233 0.152056 0.017681 16.668264 1.938170 2.667912 0.310222 37.304168 4.337694
1997-01-01 12.543075 1.687409 0.117587 0.015819 14.582981 1.961836 2.350538 0.316216 32.845819 4.418720
1998-01-01 12.473152 1.729471 0.101749 0.014108 16.048882 2.225265 2.573537 0.356835 35.442680 4.914321
1999-01-01 10.829801 1.748309 0.087592 0.014140 15.672819 2.530141 2.521386 0.407040 33.328401 5.380370
2000-01-01 8.584429 1.338433 0.070974 0.011066 13.272327 2.069341 2.211257 0.344766 27.941013 4.356395
2001-01-01 9.810942 1.299166 0.084965 0.011251 15.512830 2.054210 2.722726 0.360544 33.188537 4.394829
2002-01-01 8.858090 1.302660 0.082730 0.012166 14.555781 2.140556 2.597713 0.382017 31.025685 4.562601
2003-01-01 9.192613 1.313230 0.107263 0.015323 16.922996 2.417571 3.012305 0.430329 33.484823 4.783546
2004-01-01 8.868360 1.213125 0.097379 0.013321 16.920220 2.314558 2.973267 0.406720 30.500774 4.172276
2005-01-01 8.142615 1.032163 0.088883 0.011267 17.844822 2.262020 3.142094 0.398294 30.421585 3.856257
2006-01-01 7.803978 1.082849 0.092805 0.012877 18.007220 2.498610 3.235929 0.449005 29.380068 4.076660
2007-01-01 8.094752 1.081787 0.107448 0.014359 19.436413 2.597493 3.526772 0.471320 29.594614 3.955041
2008-01-01 7.881507 1.114945 0.085181 0.012050 19.117827 2.704473 3.581007 0.506582 26.734478 3.781951
2009-01-01 7.339090 1.163025 0.086598 0.013723 17.895264 2.835861 3.390638 0.537314 22.528409 3.570076
2010-01-01 8.776553 1.049827 0.109436 0.013090 22.022631 2.634286 4.162182 0.497869 23.449198 2.804928
2011-01-01 7.259141 1.090969 0.085128 0.012794 18.865598 2.835292 3.598894 0.540874 18.631239 2.800071
2012-01-01 4.150356 0.007887 1.709978 0.008203 31.015892 0.007540 5.301648 0.319376 27.069449 1.559032
2013-01-01 4.046147 0.006812 1.727763 0.008069 31.779851 0.006665 5.097186 0.297145 24.914120 1.267578
2014-01-01 3.985527 0.005130 1.686796 0.006256 32.548314 0.005236 5.350233 0.235693 23.662646 0.900622
2015-01-01 4.005459 0.005207 1.647669 0.004212 33.126938 0.005270 5.236107 0.241295 23.043143 0.884316
2016-01-01 3.797205 0.005434 1.406961 0.005269 34.159907 0.005874 5.362243 0.274347 23.358710 0.962408
2017-01-01 4.051529 0.005232 1.614963 0.004192 34.237765 0.005279 5.098931 0.247521 23.216320 0.888065
2018-01-01 4.070484 0.005765 1.612369 0.003556 33.997470 0.005787 5.398052 0.277470 23.190191 0.985230
2019-01-01 4.065171 0.005234 1.630729 0.004419 34.030278 0.005262 5.185956 0.255466 23.189085 0.891994
2020-01-01 4.031308 0.005198 1.760872 0.004379 34.081194 0.005265 5.130561 0.264462 23.473584 0.859323
2021-01-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 
#Invent_total.drop(["1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999", 
#                   "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009",
#                   "2010", "2011"], inplace=True)

InTEM_total.drop(["1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999", 
                  "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", 
                  "2010", "2011"], inplace=True)

df_fgas.drop(["1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999", 
                  "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", 
                  "2010", "2011"], inplace=True)

Here is a map of the DECC network:

_images/Main_Page_45_0.png